Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	47,166
2	South Dakota	42,052
3	Louisiana	39,279
4	Mississippi	38,262
5	Alabama	36,897
6	Iowa	36,004
7	Florida	35,933
8	Tennessee	34,792
9	Arkansas	34,506
10	Wisconsin	34,432
11	South Carolina	32,736
12	Idaho	32,501
13	Arizona	32,410
14	Nebraska	32,315
15	Utah	31,682
16	Georgia	31,408
17	Texas	30,994
18	Nevada	30,532
19	Illinois	29,209
20	Oklahoma	28,773
21	Rhode Island	28,430
22	Missouri	28,003
23	Kansas	26,815
24	New Jersey	25,581
25	New York	25,522
26	Montana	24,888
27	North Carolina	24,364
28	Delaware	24,325
29	Indiana	23,787
30	District of Columbia	23,533
31	Minnesota	23,036
32	Maryland	23,022
33	California	22,836
34	Massachusetts	21,513
35	Kentucky	21,497
36	Virginia	20,067
37	New Mexico	19,156
38	Puerto Rico	19,095
39	Connecticut	18,526
40	Wyoming	18,220
41	Alaska	17,862
42	Michigan	17,235
43	Ohio	16,506
44	Colorado	15,987
45	Pennsylvania	15,283
46	Washington	13,944
47	West Virginia	11,936
48	Hawaii	10,323
49	Oregon	9,805
50	New Hampshire	7,436
51	Maine	4,534
52	Vermont	3,230

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Dakota	1,034
2	North Dakota	994
3	Montana	748
4	Wisconsin	720
5	Wyoming	586
6	Idaho	546
7	Nebraska	534
8	Utah	512
9	Iowa	463
10	Rhode Island	462
11	Alabama	434
12	Arkansas	408
13	Tennessee	381
14	Missouri	378
15	Kansas	370
16	Illinois	367
17	Oklahoma	362
18	New Mexico	361
19	Indiana	349
20	Alaska	327
21	Kentucky	321
22	Mississippi	286
23	Minnesota	256
24	Nevada	255
25	Puerto Rico	244
26	Colorado	237
27	Texas	221
28	Ohio	208
29	Michigan	206
30	Louisiana	203
31	North Carolina	202
32	South Carolina	197
33	Florida	176
34	Georgia	167
35	West Virginia	162
36	Connecticut	149
37	Pennsylvania	147
38	Virginia	137
39	Arizona	136
40	Massachusetts	136
41	California	128
42	Delaware	123
43	New Jersey	121
44	Maryland	107
45	Washington	102
46	Oregon	95
47	New York	90
48	District of Columbia	77
49	Hawaii	74
50	New Hampshire	69
51	Vermont	32
52	Maine	26

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,832
2	New York	1,698
3	Massachusetts	1,426
4	Connecticut	1,283
5	Louisiana	1,251
6	Rhode Island	1,111
7	Mississippi	1,087
8	District of Columbia	909
9	Arizona	805
10	Illinois	765
11	Florida	760
12	Michigan	749
13	South Carolina	733
14	Georgia	714
15	Delaware	696
16	Pennsylvania	679
17	Maryland	674
18	Texas	616
19	Indiana	607
20	Arkansas	590
21	Alabama	583
22	North Dakota	583
23	Nevada	564
24	Iowa	516
25	New Mexico	457
26	Missouri	450
27	Tennessee	445
28	Ohio	443
29	California	438
30	Minnesota	419
31	Virginia	414
32	South Dakota	402
33	North Carolina	394
34	Colorado	387
35	New Hampshire	346
36	Kansas	334
37	Kentucky	327
38	Idaho	317
39	Washington	314
40	Oklahoma	311
41	Nebraska	307
42	Wisconsin	305
43	Montana	264
44	Puerto Rico	247
45	West Virginia	235
46	Utah	176
47	Oregon	154
48	Hawaii	146
49	Wyoming	117
50	Maine	108
51	Vermont	92
52	Alaska	87

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	12
2	Montana	9
3	South Dakota	9
4	Kansas	8
5	Wisconsin	6
6	Arkansas	5
7	Idaho	5
8	Iowa	5
9	Missouri	5
10	South Carolina	5
11	Tennessee	5
12	Hawaii	4
13	Indiana	4
14	Kentucky	4
15	Minnesota	4
16	Mississippi	4
17	Rhode Island	4
18	Wyoming	4
19	Alabama	3
20	Delaware	3
21	Florida	3
22	Illinois	3
23	Louisiana	3
24	Massachusetts	3
25	Michigan	3
26	Nebraska	3
27	North Carolina	3
28	Oklahoma	3
29	California	2
30	Georgia	2
31	New Mexico	2
32	Ohio	2
33	Pennsylvania	2
34	Puerto Rico	2
35	Texas	2
36	Virginia	2
37	West Virginia	2
38	Arizona	1
39	Colorado	1
40	Connecticut	1
41	Maryland	1
42	Nevada	1
43	New Jersey	1
44	Oregon	1
45	Utah	1
46	Alaska	0
47	District of Columbia	0
48	Maine	0
49	New Hampshire	0
50	New York	0
51	Vermont	0
52	Washington	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	176,597	1	99
Chattahoochee	Georgia	159,072	2	99
Trousdale	Tennessee	155,796	3	99
Lafayette	Florida	150,677	4	99
Lake	Tennessee	139,681	5	99
Davidson	Tennessee	47,140	269	91
Richland	South Carolina	40,706	421	86
York	South Carolina	22,987	1471	53
Orange	California	18,852	1836	41
Pierce	Washington	11,963	2463	21

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Emporia city	Virginia	5,238	1	99
Hancock	Georgia	5,203	2	99
Kenedy	Texas	4,950	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	637	831	73
Davidson	Tennessee	480	1163	62
Orange	California	453	1227	60
York	South Carolina	363	1462	53
Pierce	Washington	271	1770	43

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.